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train_card_spot.py
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train_card_spot.py
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import os
import sys
import json
import argparse
import time
import numpy as np
import torch
torch.backends.cudnn.enabled = False
import torch.nn as nn
import torch.nn.functional as F
import random
from configs.config_transformer import cfg, merge_cfg_from_file
from datasets.datasets import create_dataset
from models.CARD import CARD
from models.transformer_decoder import DynamicSpeaker
from utils.logger import Logger
from utils.utils import AverageMeter, accuracy, set_mode, save_checkpoint, \
LanguageModelCriterion, decode_sequence, decode_sequence_transformer, decode_beams, \
build_optimizer, coco_gen_format_save, one_hot_encode, \
EntropyLoss, LabelSmoothingLoss
from utils.vis_utils import visualize_att
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
# Load config
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', required=True)
parser.add_argument('--visualize', action='store_true')
parser.add_argument('--visualize_every', type=int, default=10)
args = parser.parse_args()
merge_cfg_from_file(args.cfg)
# Device configuration
use_cuda = torch.cuda.is_available()
gpu_ids = cfg.gpu_id
torch.backends.cudnn.enabled = False
default_gpu_device = gpu_ids[0]
torch.cuda.set_device(default_gpu_device)
device = torch.device("cuda" if use_cuda else "cpu")
# Experiment configuration
exp_dir = cfg.exp_dir
exp_name = cfg.exp_name
if not os.path.exists(exp_dir):
os.makedirs(exp_dir)
output_dir = os.path.join(exp_dir, exp_name)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
cfg_file_save = os.path.join(output_dir, 'cfg.json')
json.dump(cfg, open(cfg_file_save, 'w'))
sample_dir = os.path.join(output_dir, 'eval_gen_samples')
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)
sample_subdir_format = '%s_samples_%d'
sent_dir = os.path.join(output_dir, 'eval_sents')
if not os.path.exists(sent_dir):
os.makedirs(sent_dir)
sent_subdir_format = '%s_sents_%d'
snapshot_dir = os.path.join(output_dir, 'snapshots')
if not os.path.exists(snapshot_dir):
os.makedirs(snapshot_dir)
snapshot_file_format = '%s_checkpoint_%d.pt'
train_logger = Logger(cfg, output_dir, is_train=True)
val_logger = Logger(cfg, output_dir, is_train=False)
random.seed(1111)
np.random.seed(1111)
torch.manual_seed(1111)
# Create model
change_detector = CARD(cfg)
change_detector.to(device)
speaker = DynamicSpeaker(cfg)
speaker.to(device)
print(change_detector)
print(speaker)
with open(os.path.join(output_dir, 'model_print'), 'w') as f:
print(change_detector, file=f)
print(speaker, file=f)
# Data loading part
train_dataset, train_loader = create_dataset(cfg, 'train')
val_dataset, val_loader = create_dataset(cfg, 'val')
train_size = len(train_dataset)
val_size = len(val_dataset)
all_params = list(change_detector.parameters()) + list(speaker.parameters())
optimizer = build_optimizer(all_params, cfg)
lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
step_size=cfg.train.optim.step_size,
gamma=cfg.train.optim.gamma)
# Train loop
t = 0
epoch = 0
set_mode('train', [change_detector, speaker])
while t < cfg.train.max_iter:
epoch += 1
print('Starting epoch %d' % epoch)
speaker_loss_avg = AverageMeter()
constraint_loss_avg = AverageMeter()
total_loss_avg = AverageMeter()
if epoch > cfg.train.scheduled_sampling_start and cfg.train.scheduled_sampling_start >= 0:
frac = (epoch - cfg.train.scheduled_sampling_start) // cfg.train.scheduled_sampling_increase_every
ss_prob_prev = ss_prob
ss_prob = min(cfg.train.scheduled_sampling_increase_prob * frac,
cfg.train.scheduled_sampling_max_prob)
speaker.ss_prob = ss_prob
if ss_prob_prev != ss_prob:
print('Updating scheduled sampling rate: %.4f -> %.4f' % (ss_prob_prev, ss_prob))
for i, batch in enumerate(train_loader):
iter_start_time = time.time()
d_feats, sc_feats, \
labels, labels_with_ignore, masks, d_img_paths, sc_img_paths = batch
batch_size = d_feats.size(0)
labels = labels.squeeze(1)
labels_with_ignore = labels_with_ignore.squeeze(1)
masks = masks.squeeze(1).float()
d_feats, sc_feats = d_feats.to(device), sc_feats.to(device)
labels, labels_with_ignore, masks = labels.to(device), labels_with_ignore.to(device), masks.to(device)
optimizer.zero_grad()
encoder_output, constraint_loss, _, _ = change_detector(d_feats, sc_feats)
loss_pos, _, att_pos = speaker._forward(encoder_output,
labels, masks, labels_with_ignore=labels_with_ignore)
speaker_loss = loss_pos
speaker_loss_val = speaker_loss.item()
constraint_loss_val = constraint_loss.item()
total_loss = speaker_loss + 0.001 * constraint_loss # 0.001
total_loss_val = total_loss.item()
speaker_loss_avg.update(speaker_loss_val, 2 * batch_size)
constraint_loss_avg.update(constraint_loss_val, 2 * batch_size)
total_loss_avg.update(total_loss_val, 2 * batch_size)
stats = {}
stats['speaker_loss'] = speaker_loss_val
stats['avg_speaker_loss'] = speaker_loss_avg.avg
stats['constraint_loss'] = constraint_loss_val
stats['avg_constraint_loss'] = constraint_loss_avg.avg
stats['total_loss'] = total_loss_val
stats['avg_total_loss'] = total_loss_avg.avg
#results, sample_logprobs = model(d_feats, q_feats, labels, cfg=cfg, mode='sample')
total_loss.backward()
if cfg.train.grad_clip != -1.0: # enable, -1 == disable
nn.utils.clip_grad_norm_(change_detector.parameters(), cfg.train.grad_clip)
nn.utils.clip_grad_norm_(speaker.parameters(), cfg.train.grad_clip)
optimizer.step()
iter_end_time = time.time() - iter_start_time
t += 1
if t % cfg.train.log_interval == 0:
train_logger.print_current_stats(epoch, i, t, stats, iter_end_time)
train_logger.plot_current_stats(
epoch,
float(i * batch_size) / train_size, stats, 'loss')
if t % cfg.train.snapshot_interval == 0:
speaker_state = speaker.state_dict()
chg_det_state = change_detector.state_dict()
checkpoint = {
'change_detector_state': chg_det_state,
'speaker_state': speaker_state,
'model_cfg': cfg
}
save_path = os.path.join(snapshot_dir,
snapshot_file_format % (exp_name, t))
save_checkpoint(checkpoint, save_path)
print('Running eval at iter %d' % t)
set_mode('eval', [change_detector, speaker])
with torch.no_grad():
test_iter_start_time = time.time()
idx_to_word = train_dataset.get_idx_to_word()
if args.visualize:
sample_subdir_path = sample_subdir_format % (exp_name, t)
sample_save_dir = os.path.join(sample_dir, sample_subdir_path)
if not os.path.exists(sample_save_dir):
os.makedirs(sample_save_dir)
sent_subdir_path = sent_subdir_format % (exp_name, t)
sent_save_dir = os.path.join(sent_dir, sent_subdir_path)
if not os.path.exists(sent_save_dir):
os.makedirs(sent_save_dir)
result_sents_pos = {}
for val_i, val_batch in enumerate(val_loader):
d_feats, sc_feats, \
labels, labels_with_ignore, masks, \
d_img_paths, sc_img_paths = val_batch
val_batch_size = d_feats.size(0)
d_feats, sc_feats = d_feats.to(device), sc_feats.to(device)
labels, labels_with_ignore, masks = labels.to(device), labels_with_ignore.to(device), masks.to(device)
encoder_output, _, att1, att2 = change_detector(d_feats, sc_feats)
speaker_output_pos, _ = speaker.sample(encoder_output)
gen_sents_pos = decode_sequence_transformer(idx_to_word, speaker_output_pos[:, 1:]) # no start
for val_j in range(speaker_output_pos.size(0)):
gts = decode_sequence_transformer(idx_to_word, labels[val_j][:, 1:])
sent_pos = gen_sents_pos[val_j]
image_id = d_img_paths[val_j].split('/')[-1]
result_sents_pos[image_id] = sent_pos
message = '%s results:\n' % d_img_paths[val_j]
message += '\t' + sent_pos + '\n'
message += '----------<GROUND TRUTHS>----------\n'
for gt in gts:
message += gt + '\n'
message += '===================================\n'
print(message)
test_iter_end_time = time.time() - test_iter_start_time
result_save_path_pos = os.path.join(sent_save_dir, 'sc_results.json')
coco_gen_format_save(result_sents_pos, result_save_path_pos)
set_mode('train', [change_detector, speaker])
lr_scheduler.step()